My back: co-designing municipal rehabilitation with and for individuals with long-lasting back problems: study protocol
Bibliographic record
Abstract
BACKGROUND: In Denmark, the organization and content of rehabilitation for people with back problems vary by municipality. Furthermore, there is no systematic evaluation of the overall effect and quality of municipal rehabilitation efforts. Since individuals with long-lasting back problems often receive multiple interventions delivered by various professionals, departments, and sectors, a coordinated effort is essential within complex systems, like municipal organizations. Therefore, the Municipality of Svendborg, the University of Southern Denmark, and UCL University College aim to co-design an improved municipal rehabilitation program with and for individuals with back problems, within the existing municipal context. The new practices developed in the program seek to better support individuals to manage everyday life with back problems, develop effective rehabilitation practices, and decrease the associated municipal costs. METHODS: My Back is a mixed-method research study, utilizing a Plan-Do-Study-Act (PDSA) approach to ensure iterative development and continuous improvement of municipal rehabilitation practices. This study is structured into several work packages (WPs) that focus on identifying experiences (WP 1), mapping current practices (WP 2), summarizing relevant literature (WP 3), conducting co-design workshops (WP 4), and the development and implementation of new rehabilitation practices via PDSA cycles (WP 5). These WPs will inform the development and implementation of new rehabilitation practices, which will be evaluated for quality and effectiveness (WP 6), and system-level changes (WP 7), followed by dissemination of results (WP 8). DISCUSSION: Through a local co-design process involving individuals with back problems, municipal professionals, and leaders, we expect strong relevance and engagement, ensuring successful implementation. The new practices aim to better support people with back problems to manage everyday life through improved rehabilitation and interdisciplinary collaboration among municipal social and health professionals. A platform model for monitoring local rehabilitation will be introduced to evaluate workability and economic implications within the target population. The knowledge and results from this study can be disseminated and adapted to public contexts throughout Denmark and potentially other countries with similar municipal healthcare structures. Sharing best practices and lessons learned from the implementation process can inform rehabilitation practices in different international settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".